cov(x, y=None, rowvar=True, bias=False, allow_masked=True, ddof=None)
Except for the handling of missing data this function does the same as numpy.cov
. For more details and examples, see numpy.cov
.
By default, masked values are recognized as such. If x and y have the same shape, a common mask is allocated: if x[i,j]
is masked, then y[i,j]
will also be masked. Setting :None:None:`allow_masked` to False will raise an exception if values are missing in either of the input arrays.
A 1-D or 2-D array containing multiple variables and observations. Each row of x represents a variable, and each column a single observation of all those variables. Also see :None:None:`rowvar` below.
An additional set of variables and observations. y has the same shape as x.
If :None:None:`rowvar` is True (default), then each row represents a variable, with observations in the columns. Otherwise, the relationship is transposed: each column represents a variable, while the rows contain observations.
Default normalization (False) is by (N-1)
, where N
is the number of observations given (unbiased estimate). If :None:None:`bias` is True, then normalization is by N
. This keyword can be overridden by the keyword ddof
in numpy versions >= 1.5.
If True, masked values are propagated pair-wise: if a value is masked in x, the corresponding value is masked in y. If False, raises a :None:None:`ValueError` exception when some values are missing.
If not None
normalization is by (N - ddof)
, where N
is the number of observations; this overrides the value implied by bias
. The default value is None
.
Raised if some values are missing and :None:None:`allow_masked` is False.
Estimate the covariance matrix.
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